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Drug Toxicity: Overview01:00

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Drug toxicity quantifies the harm a compound causes to an organism, varying by dose and potentially impacting whole systems or specific organs like the liver. Toxic reactions may arise from venomous insect or spider bites, with effects ranging from mild symptoms to severe outcomes such as brain damage or death. Common forms of acute poisoning include ethanol intoxication and overdose of pain or fever medications, with substances like GHB and heroin being particularly lethal at doses close to...
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Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method.

Hongkai Zhao1, Sen Qiu1, Meirong Bai2

  • 1Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian 116024, China; School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China.

Computers in Biology and Medicine
|March 26, 2024
PubMed
Summary

This study introduces a novel approach using data augmentation and transfer learning to predict the toxicity of Mongolian medicine Gunqile-7. The method significantly improves prediction accuracy on small datasets, crucial for drug safety assessment.

Keywords:
Data augmentationFeature selectionGunqile-7Mongolian medicineToxicity classificationTransfer learning

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Area of Science:

  • Pharmacology and Computational Toxicology
  • Traditional Mongolian Medicine

Background:

  • Drug-induced diseases are a significant aspect of iatrogenic illness.
  • Assessing the toxicity of traditional medicines like Gunqile-7 is vital for patient safety.
  • Traditional animal testing for drug toxicity is costly and yields small datasets.

Purpose of the Study:

  • To develop an efficient computational method for predicting the toxicity of Gunqile-7.
  • To overcome the limitations of small sample sizes in pharmacological trials using advanced machine learning.
  • To enhance the safety assessment of traditional Mongolian medicine.

Main Methods:

  • Employed data augmentation to expand the limited dataset for Gunqile-7 toxicity.
  • Utilized transfer learning with a one-dimensional convolutional neural network for model training.
  • Applied Support Vector Machine-Recursive Feature Elimination for effective feature selection.

Main Results:

  • The proposed method demonstrated improved accuracy in predicting Gunqile-7 toxicity.
  • Achieved up to a 9 percentage point increase in accuracy compared to models without transfer learning.
  • Successfully reduced the number of required training samples through data augmentation.

Conclusions:

  • The combination of data augmentation and transfer learning is effective for toxicity prediction with small datasets.
  • This approach offers a cost-efficient and accurate alternative to traditional animal testing for drug safety.
  • The study validates the utility of computational methods in evaluating traditional medicines.